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Predicting protein function using multiple kernels

Yu, Guoxian, Rangwala, Huzefa, Domeniconi, Carlotta, Zhang, Guoji and Zhang, Zili 2015, Predicting protein function using multiple kernels, IEEE/ACM transactions on computational biology and bioinformatics, vol. 12, no. 1, pp. 219-233, doi: 10.1109/TCBB.2014.2351821.

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Title Predicting protein function using multiple kernels
Author(s) Yu, Guoxian
Rangwala, Huzefa
Domeniconi, Carlotta
Zhang, Guoji
Zhang, ZiliORCID iD for Zhang, Zili orcid.org/0000-0002-8721-9333
Journal name IEEE/ACM transactions on computational biology and bioinformatics
Volume number 12
Issue number 1
Start page 219
End page 233
Total pages 15
Publisher Institute of Electrical and Electronics Engineers
Place of publication Champaign, Ill.
Publication date 2015
ISSN 1545-5963
Keyword(s) multi-label learning
multiple kernels
Protein function prediction
Summary High-throughput experimental techniques provide a wide variety of heterogeneous proteomic data sources. To exploit the information spread across multiple sources for protein function prediction, these data sources are transformed into kernels and then integrated into a composite kernel. Several methods first optimize the weights on these kernels to produce a composite kernel, and then train a classifier on the composite kernel. As such, these approaches result in an optimal composite kernel, but not necessarily in an optimal classifier. On the other hand, some approaches optimize the loss of binary classifiers and learn weights for the different kernels iteratively. For multi-class or multi-label data, these methods have to solve the problem of optimizing weights on these kernels for each of the labels, which are computationally expensive and ignore the correlation among labels. In this paper, we propose a method called Predicting Protein Function using Multiple K ernels (ProMK). ProMK iteratively optimizes the phases of learning optimal weights and reduces the empirical loss of multi-label classifier for each of the labels simultaneously. ProMK can integrate kernels selectively and downgrade the weights on noisy kernels. We investigate the performance of ProMK on several publicly available protein function prediction benchmarks and synthetic datasets. We show that the proposed approach performs better than previously proposed protein function prediction approaches that integrate multiple data sources and multi-label multiple kernel learning methods. The codes of our proposed method are available at https://sites.google.com/site/guoxian85/promk.
Language eng
DOI 10.1109/TCBB.2014.2351821
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071815

Document type: Journal Article
Collection: School of Information Technology
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